Jupyter Notebooks

Notebook setup, magic commands, and data visualization examples.

Setup & Installation

Installation & Basic Setup

# Install Jupyter
pip install jupyter

# Install additional packages
pip install jupyterlab
pip install ipywidgets
pip install matplotlib seaborn pandas numpy

# Start Jupyter Notebook
jupyter notebook

# Start JupyterLab (modern interface)
jupyter lab

# Create a new notebook
jupyter notebook my_notebook.ipynb

# Convert notebook to different formats
jupyter nbconvert --to html my_notebook.ipynb
jupyter nbconvert --to pdf my_notebook.ipynb
jupyter nbconvert --to python my_notebook.ipynb

# Install extensions
pip install jupyter_contrib_nbextensions
jupyter contrib nbextension install --user

Magic Commands

Line Magic Commands

# Timing and profiling
%time sum(range(1000000))
%timeit sum(range(1000000))

# System commands
%pwd                    # Current directory
%ls                     # List files
%cd /path/to/directory  # Change directory
%mkdir new_folder       # Create directory

# Environment and variables
%env                    # Show environment variables
%env MY_VAR=value       # Set environment variable
%who                    # Show all variables
%whos                   # Show variables with details
%del variable_name      # Delete variable

# History and debugging
%history                # Show command history
%debug                  # Enter debugger
%pdb on                 # Auto-enter debugger on error

# File operations
%load filename.py       # Load Python file
%run filename.py        # Run Python file
%save filename.py 1-5   # Save lines 1-5 to file

# Display options
%matplotlib inline      # Show plots inline
%matplotlib notebook    # Interactive plots
%config InlineBackend.figure_format = 'svg'  # SVG format

Cell Magic Commands

# Run different languages
%%bash
echo "Hello from Bash!"
ls -la

%%html
<h1>Hello from HTML!</h1>
<p>This is HTML content in a notebook cell.</p>

%%javascript
console.log("Hello from JavaScript!");
alert("Hello World!");

%%latex
\begin{equation}
E = mc^2
\end{equation}

# File operations
%%writefile my_script.py
def hello():
    print("Hello from external file!")

%%writefile -a my_script.py
def goodbye():
    print("Goodbye!")

# Capture output
%%capture captured_output
print("This output will be captured")
result = 42

# Show captured output
captured_output.show()

# Suppress output
%%capture
import warnings
warnings.filterwarnings('ignore')
print("This won't be displayed")

Data Visualization

Matplotlib Examples

import matplotlib.pyplot as plt
import numpy as np

# Basic plot
x = np.linspace(0, 10, 100)
y = np.sin(x)
plt.figure(figsize=(10, 6))
plt.plot(x, y, 'b-', linewidth=2, label='sin(x)')
plt.xlabel('X values')
plt.ylabel('Y values')
plt.title('Sine Wave')
plt.legend()
plt.grid(True)
plt.show()

# Multiple subplots
fig, axes = plt.subplots(2, 2, figsize=(12, 8))

# Subplot 1: Line plot
x = np.linspace(0, 10, 100)
axes[0, 0].plot(x, np.sin(x))
axes[0, 0].set_title('Sine Wave')

# Subplot 2: Scatter plot
x = np.random.randn(100)
y = np.random.randn(100)
axes[0, 1].scatter(x, y, alpha=0.6)
axes[0, 1].set_title('Scatter Plot')

# Subplot 3: Histogram
data = np.random.randn(1000)
axes[1, 0].hist(data, bins=30, alpha=0.7)
axes[1, 0].set_title('Histogram')

# Subplot 4: Bar chart
categories = ['A', 'B', 'C', 'D']
values = [23, 45, 56, 78]
axes[1, 1].bar(categories, values)
axes[1, 1].set_title('Bar Chart')

plt.tight_layout()
plt.show()

Seaborn Examples

import seaborn as sns
import pandas as pd
import numpy as np

# Set style
sns.set_style("whitegrid")
sns.set_palette("husl")

# Load sample data
tips = sns.load_dataset("tips")

# Distribution plots
plt.figure(figsize=(12, 4))

plt.subplot(1, 3, 1)
sns.histplot(data=tips, x="total_bill", kde=True)
plt.title("Total Bill Distribution")

plt.subplot(1, 3, 2)
sns.boxplot(data=tips, x="day", y="total_bill")
plt.title("Total Bill by Day")

plt.subplot(1, 3, 3)
sns.violinplot(data=tips, x="day", y="total_bill", hue="sex")
plt.title("Total Bill by Day and Sex")

plt.tight_layout()
plt.show()

# Correlation heatmap
plt.figure(figsize=(8, 6))
numeric_data = tips.select_dtypes(include=[np.number])
correlation_matrix = numeric_data.corr()
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', center=0)
plt.title("Correlation Heatmap")
plt.show()

# Pair plot
sns.pairplot(tips, hue="sex", diag_kind="hist")
plt.show()

Interactive Widgets

Interactive Controls

import ipywidgets as widgets
from IPython.display import display
import matplotlib.pyplot as plt
import numpy as np

# Basic interactive plot
def plot_sine_wave(frequency, amplitude):
    x = np.linspace(0, 4*np.pi, 1000)
    y = amplitude * np.sin(frequency * x)
    
    plt.figure(figsize=(10, 6))
    plt.plot(x, y, 'b-', linewidth=2)
    plt.title(f'Sine Wave: f={frequency}, A={amplitude}')
    plt.xlabel('X')
    plt.ylabel('Y')
    plt.grid(True)
    plt.show()

# Create interactive widgets
frequency_slider = widgets.FloatSlider(
    value=1.0,
    min=0.1,
    max=5.0,
    step=0.1,
    description='Frequency:'
)

amplitude_slider = widgets.FloatSlider(
    value=1.0,
    min=0.1,
    max=3.0,
    step=0.1,
    description='Amplitude:'
)

# Interactive widget
interactive_plot = widgets.interactive(
    plot_sine_wave,
    frequency=frequency_slider,
    amplitude=amplitude_slider
)

display(interactive_plot)

# Dropdown and text widgets
def analyze_data(dataset, operation):
    if dataset == 'Iris':
        data = sns.load_dataset('iris')
    elif dataset == 'Tips':
        data = sns.load_dataset('tips')
    else:
        data = sns.load_dataset('titanic')
    
    if operation == 'Info':
        return data.info()
    elif operation == 'Describe':
        return data.describe()
    else:
        return data.head()

dataset_dropdown = widgets.Dropdown(
    options=['Iris', 'Tips', 'Titanic'],
    value='Iris',
    description='Dataset:'
)

operation_dropdown = widgets.Dropdown(
    options=['Info', 'Describe', 'Head'],
    value='Info',
    description='Operation:'
)

interactive_analysis = widgets.interactive(
    analyze_data,
    dataset=dataset_dropdown,
    operation=operation_dropdown
)

display(interactive_analysis)

Quick Reference

Keyboard Shortcuts:

• Shift + Enter - Run cell
• Ctrl + Enter - Run cell (stay)
• A - Insert cell above
• B - Insert cell below
• DD - Delete cell
• M - Markdown mode
• Y - Code mode

Useful Extensions:

• jupyter_contrib_nbextensions
• jupyterlab-git - Git integration
• jupyterlab-lsp - Language server
• jupyterlab-drawio - Diagrams
• jupyterlab-toc - Table of contents